During the COVID-19 pandemic, many institutions such as universities and workplaces implemented testing regimens with every member of some population tested longitudinally, and those testing positive isolated for some time. Although the primary purpose of such regimens was to suppress disease spread by identifying and isolating infectious individuals, testing results were often also used to obtain prevalence and incidence estimates. Such estimates are helpful in risk assessment and institutional planning and various estimation procedures have been implemented, ranging from simple test-positive rates to complex dynamical modeling. Unfortunately, the popular test-positive rate is a biased estimator of prevalence under many seemingly innocuous longitudinal testing regimens with isolation. We illustrate how such bias arises and identify conditions under which the test-positive rate is unbiased. Further, we identify weaker conditions under which prevalence is identifiable and propose a new estimator of prevalence under longitudinal testing. We evaluate the proposed estimation procedure via simulation study and illustrate its use on a dataset derived by anonymizing testing data from The Ohio State University.

翻译：在COVID-19疫情期间，许多机构（如大学和工作场所）实施长向测试方案，对人群中的每个成员进行测试，对测试呈阳性的人进行隔离一段时间。尽管此类方案的主要目的是通过识别和隔离有感染力的个体来抑制疾病传播，但测试结果通常也用于获得患病率和发病率估计值。这些估计值有助于风险评估和机构规划，并实施各种估计程序，范围从简单的测试阳性率到复杂的动力模型。不幸的是，流行的测试阳性率是在许多看似无害的纵向测试计划中是患病率的偏估计。我们阐明了这种偏差是如何产生的，并识别了测试阳性率无偏估计的条件。此外，我们还确定了更弱的条件，确定了在纵向测试下患病率是可识别的，并提出了一种新的估计纵向测试下患病率的估计量。我们通过模拟研究评估了所提出的估计程序，并通过从俄亥俄州立大学的测试数据得出的数据集进行了说明。